We implemented our architecture with Scikit-learn [1]}. To obtain test predictions, we applied the following steps separately for each corpus:
We perform feature selection via recursive feature elimination [2]} on the respective development split. We use a random forest classifier tasked with classifying mention pairs as "coreferring" / "not coreferring" as an auxiliary task for this stage.
We then identified the best classification algorithm to use as the probabilistic mention classifier. We tested logistic regression, a mlp, a probabilistic SVM and XGBoost [3]}. We tuned the hyperparameters of each classifier via repeated 6-fold cross-validation for 24 hours on the respective training split.Details on the hyperparameter optimization procedure are provided in Appendix G.. Using the best classifier, we optimized the hyperparameters of the agglomerative clustering step (i.e., the linkage method, cluster criterion and threshold) for another 24 hours on the training split. For each experiment, we train five models with different random seeds to account for non-determinism. At test time we evaluate each of the five models and report the mean of each evaluation metric.N.B. This applies to every result originating from this model throughout this work. We will therefore not point this out further.
